1 |
张永杰, 崔博, 王明振, 等. 水陆两栖飞机着水试验与理论分析方法研究进展[J]. 航空学报, 2023, 44(21): 528665.
|
|
ZHANG Y J, CUI B, WANG M Z, et al. Research progress of amphibious aircraft water landing test and theoretical analysis methods[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(21): 528665 (in Chinese).
|
2 |
李勐, 陈星伊, 陈吉昌, 等. 波浪情况下民机水上迫降性能数值分析[J]. 航空学报, 2024, 45(2): 128604.
|
|
LI M, CHEN X Y, CHEN J C, et al. Numerical analysis of civil aircraft ditching performance in wave condition[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 128604 (in Chinese).
|
3 |
寇家庆, 张伟伟, 高传强. 基于POD和DMD方法的跨声速抖振模态分析[J]. 航空学报, 2016, 37(9): 2679-2689.
|
|
KOU J Q, ZHANG W W, GAO C Q. Modal analysis of transonic buffet based on POD and DMD method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(9): 2679-2689 (in Chinese).
|
4 |
DOWELL E H. Eigenmode analysis in unsteady aerodynamics-Reduced-order models[J]. AIAA Journal, 1996, 34(8): 1578-1583.
|
5 |
刘溢浪, 张伟伟, 蒋跃文, 等. 一种基于增量径向基函数插值的流场重构方法[J]. 力学学报, 2014, 46(5): 694-702.
|
|
LIU Y L, ZHANG W W, JIANG Y W, et al. A reconstruction method for finite volume flow field solving based on incremental radial basis functions[J]. Chinese Journal of Theoretical and Applied Mechanics, 2014, 46(5): 694-702 (in Chinese).
|
6 |
陈皓, 郭明明, 田野, 等. 卷积神经网络在流场重构研究中的进展[J]. 力学学报, 2022, 54(9): 2343-2360.
|
|
CHEN H, GUO M M, TIAN Y, et al. Progress of convolution neural networks in flow field reconstruction[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2343-2360 (in Chinese).
|
7 |
LIU B, TANG J P, HUANG H B, et al. Deep learning methods for super-resolution reconstruction of turbulent flows[J]. Physics of Fluids, 2020, 32(2): 025105.
|
8 |
QU X Y, LIU Z J, YU B Y, et al. Predicting pressure coefficients of wing surface based on the transfer of spatial dependency[J]. AIP Advances, 2022, 12(5): 055225.
|
9 |
FUKAMI K, FUKAGATA K, TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of Fluid Mechanics, 2019, 870: 106-120.
|
10 |
KONG C, CHANG J T, WANG Z A, et al. Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks[J]. AIP Advances, 2021, 11(6): 065321.
|
11 |
GUO M M, CHEN E D, TIAN Y, et al. Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions[J]. Physics of Fluids, 2022, 34(6): 065111.
|
12 |
KASHEFI A, REMPE D, GUIBAS L J. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries[J]. Physics of Fluids, 2021, 33(2): 027104.
|
13 |
SUN L N, WANG J X. Physics-constrained Bayesian neural network for fluid flow reconstruction with sparse and noisy data[J]. Theoretical and Applied Mechanics Letters, 2020, 10(3): 161-169.
|
14 |
HAN R K, WANG Y X, ZHANG Y, et al. A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network[J]. Physics of Fluids, 2019, 31(12): 127101.
|
15 |
HASEGAWA K, FUKAMI K, MURATA T, et al. Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes[J]. Theoretical and Computational Fluid Dynamics, 2020, 34(4): 367-383.
|
16 |
DENG Z W, CHEN Y J, LIU Y Z, et al. Time-resolved turbulent velocity field reconstruction using a long short-term memory (LSTM)-based artificial intelligence framework[J]. Physics of Fluids, 2019, 31(7): 075108.
|
17 |
YU J, HESTHAVEN J S. Flowfield reconstruction method using artificial neural network[J]. AIAA Journal, 2019, 57(2): 482-498.
|
18 |
VAN DEN OORD A, DIELEMAN S, ZEN H G, et al. WaveNet: A generative model for raw audio[DB/OL]. arXiv preprint: 1609.03499, 2016.
|
19 |
BAI S J, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[DB/OL]. arXiv preprint: 1803.01271, 2018.
|
20 |
SOHL-DICKSTEIN J, WEISS E A, MAHESWARANATHAN N, et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics[DB/OL]. arXiv preprint: 1503.03585, 2015.
|
21 |
HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[DB/OL]. arXiv preprint: 2006.11239, 2020.
|
22 |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]∥ 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 770-778.
|